Methodologies of Symbolic Computation

被引:0
作者
Davenport, James [1 ]
机构
[1] Univ Bath, Bath, Avon, England
来源
ARTIFICIAL INTELLIGENCE AND SYMBOLIC COMPUTATION (AISC 2018) | 2018年 / 11110卷
关键词
Computer algebra; Benchmarking; FACTORING POLYNOMIALS; ALGORITHM; BOUNDS;
D O I
10.1007/978-3-319-99957-9_2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The methodologies of computer algebra are about making algebra (in the broad sense) algorithmic, and efficient as well. There are ingenious algorithms, even in the obvious settings, and also mechanisms where problems are translated into other (generally smaller) settings, solved there, and translated back. Much of the efficiency of modern systems comes from these translations. One of the major challenges is sparsity, and the complexity of algorithms in the sparse setting is often unknown, as many problems are NP-hard, or much worse. In view of this, it is argued that the traditional complexity-theoretic method of measuring progress has its limits, and computer algebra should look to the work of the SAT community, with its large families of benchmarks and serious contests, for lessons.
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页码:19 / 33
页数:15
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